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Author: Sahir Bhatnagar (sahir.bhatnagar@gmail.com)
Notes:
This vignette was built with R markdown and knitr. The source code for this vignette can be found here.
In the interactive manhattan, qq and volcano plots below, we only use a subset (random sample of SNPs on chromosomes 4 to 7) of the HapMap data included in this package. This is to reduce the size of the compiled vignette. To reduce the package size, we don’t output any plots here. Please see https://sahirbhatnagar.com/manhattanly/articles/web_only/manhattanly_full.html for the full vignette with plots.
Manhattan, Q-Q and volcano plots are popular graphical methods for visualizing results from high-dimensional data analysis such as a (epi)genome wide asssociation study (GWAS or EWAS), in which p-values, Z-scores, test statistics are plotted on a scatter plot against their genomic position. Manhattan plots are used for visualizing potential regions of interest in the genome that are associated with a phenotype. Q-Q plots tell us about the distributional assumptions of the observed test statistics. Volcano plots are the negative log10 p-values plotted against their effect size, odds ratio or log fold-change. They are used to identify clinically meaningful markers in genomic experiments, i.e., markers that are statistically significant and have an effect size greater than some threshold.
Interactive manhattan, Q-Q and volcano plots allow the inspection of specific value (e.g. rs number or gene name) by hovering the mouse over a point, as well as zooming into a region of the genome (e.g. a chromosome) by dragging a rectangle around the relevant area.
This pacakge creates interactive Q-Q, manhattan and volcano plots that are usable from the R console, the RStudio viewer pane, R Markdown documents, in Dash apps, Shiny apps, embeddable in websites and can be exported as .png files. By hovering the mouse over a point, you can see annotation information such as the SNP identifier and GENE name. You can also drag a rectangle to zoom in on a region of interest and then export the image as a .png file.
This work is based on the qqman R package and the plotly.js engine. It produces similar manhattan and Q-Q plots as the qqman::manhattan and qqman::qq functions; the main difference here is being able to interact with the plot, including extra annotation information and seamless integration with HTML.
You can install manhattanly from CRAN:
install.packages("manhattanly")Alternatively, you can install the development version of manhattanly from GitHub with:
if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh("sahirbhatnagar/manhattanly")The manhattanly package ships with an example dataset called HapMap. See help(HapMap) for more details about how this dataset was created. Here is what the HapMap dataset looks like:
# load the manhattanly library
library(manhattanly)## See example usage at http://sahirbhatnagar.com/manhattanly/set.seed(12345)
HapMap.subset <- subset(HapMap, CHR %in% 4:7)
# for highlighting SNPs of interest
significantSNP <- sample(HapMap.subset$SNP, 20)
head(HapMap.subset)## CHR BP P SNP ZSCORE EFFECTSIZE GENE DISTANCE
## 3300 4 336758 0.7869011 rs6821220 0.2703 -0.1437 ZNF141 0
## 3301 4 992125 0.5116233 rs6855233 0.6563 -0.0128 FGFRL1 3634
## 3302 4 1155741 0.7977199 rs922697 0.2563 -0.1170 SPON2 0
## 3303 4 1302267 0.3590100 rs10025665 0.9173 0.1079 MAEA 0
## 3304 4 1388897 0.1824547 rs11731672 1.3332 0.1005 CRIPAK 9116
## 3305 4 1814818 0.4673265 rs6599401 0.7268 0.0625 LETM1 0dim(HapMap.subset)## [1] 3410 8The required columns to create a manhattan plot are the chromosome, base-pair position and p-value. By default, the manhattanly function assumes these columns are named CHR, BP and P (but these can be specified by the user if they are different)
Create an interactive manhattan plot using one command:
manhattanly(HapMap.subset, snp = "SNP", gene = "GENE")The arguments snp = "SNP" and gene = "GENE" specify that we want to add snp and gene information to each point. This information is found in the columns names "SNP" and "GENE" in the HapMap dataset. See help(manhattanly) for a full list of options.
Similarly, we can create an interactive Q-Q plot using one command (See help(qqly) for a full list of options):
qqly(HapMap.subset, snp = "SNP", gene = "GENE")You can then save the plot as a .png file by clicking on the camera icon in the toolbar (which appears when you hover your mouse over it).
You can also make a volcano plot which by default, highlights the points greater than the default genomewideline and effect_size_line arguments:
volcanoly(HapMap.subset, snp = "SNP", gene = "GENE", effect_size = "EFFECTSIZE")We can also highlight SNPs of interest using the highlight argument. This package comes with a list of SNPs of interest called significantSNP (see help(significantSNP) for more details). To highlight these SNPs we simply pass this vector to the highlight argument (note that these SNPs need to be present in the "SNP" column of your data). We omit the plot here due to size constraints of the package on CRAN:
manhattanly(HapMap.subset, snp = "SNP", gene = "GENE", highlight = significantSNP)You can add up to 4 annotations. In the following manhattan plot we add the snp, gene, the distance to the nearest gene and the effect size. The same annotations can also be added to Q-Q and volcano plots:
manhattanly(HapMap.subset, snp = "SNP", gene = "GENE",
annotation1 = "DISTANCE", annotation2 = "EFFECTSIZE",
highlight = significantSNP)The annotations in the previous plots only appear when we hover the mouse over the point. Once we have identified a SNP, or a few SNPs of interest we want to explicitly show the annotation information and save the plot. The output of the manhattanly function is an object which can be further manipulated using the %>% operator from the magrittr package:
library(magrittr)
p <- manhattanly(HapMap.subset, snp = "SNP", gene = "GENE",
annotation1 = "DISTANCE", annotation2 = "EFFECTSIZE",
highlight = significantSNP)
# get the x and y coordinates from the pre-processed data
plotData <- manhattanr(HapMap.subset, snp = "SNP", gene = "GENE")[["data"]]
# annotate the smallest p-value
annotate <- plotData[which.min(plotData$P),]
# x and y coordinates of SNP with smallest p-value
xc <- annotate$pos
yc <- annotate$logp
p %>% plotly::layout(annotations = list(
list(x = xc, y = yc,
text = paste0(annotate$SNP,"<br>","GENE: ",annotate$GENE),
font = list(family = "serif", size = 10))))You can then save the plot as a .png file by clicking on the camera icon in the toolbar (which appears when you hover your mouse over it).
knitr and R MarkdownR Markdown is a an R software package that allows the creation of dynamic documents, i.e., embed R code with text to create fully reproducible reports. Furthermore it allows easy creation of HTML reports without knowing how to code in HTML (such as this vignette). This means you can embed interactive manhattan, qq and volcano plots in HTML reports using the manhattanly package. For example, to embed the above manhattan plot I included the following code chunk in the .Rmd document:
```{r}
library(plotly)
manhattanly(subset(HapMap, CHR %in% 4:7), snp = "SNP", gene = "GENE")
```The manhattanly package splits up the data pre-processing from the rendering of the plot object (inspired by the heatmaply package. These are done by the manhattanr, qqr and volcanor functions:
# create an object of class `manhattanr`
manhattanrObject <- manhattanr(HapMap)
# whats in there
str(manhattanrObject)## List of 10
## $ data :'data.frame': 14412 obs. of 6 variables:
## ..$ CHR : int [1:14412] 1 1 1 1 1 1 1 1 1 1 ...
## ..$ BP : int [1:14412] 937641 1136887 2116240 2310562 2681715 2917484 2942700 3298358 3501155 3676178 ...
## ..$ P : num [1:14412] 0.335 0.246 0.823 0.493 0.605 ...
## ..$ logp : num [1:14412] 0.4745 0.6093 0.0844 0.307 0.218 ...
## ..$ pos : num [1:14412] 937641 1136887 2116240 2310562 2681715 ...
## ..$ index: num [1:14412] 1 1 1 1 1 1 1 1 1 1 ...
## $ xlabel : chr "Chromosome"
## $ ticks : num [1:23] 1.24e+08 3.68e+08 5.88e+08 7.83e+08 9.68e+08 ...
## $ labs : int [1:23] 1 2 3 4 5 6 7 8 9 10 ...
## $ nchr : int 23
## $ pName : chr "P"
## $ snpName : logi NA
## $ geneName : logi NA
## $ annotation1Name: logi NA
## $ annotation2Name: logi NA
## - attr(*, "class")= chr "manhattanr"# the data used for plotting is a data.frame
# this data.frame can be used with any graphics function such as in base R
# you do not need to use plotly
head(manhattanrObject[["data"]])## CHR BP P logp pos index
## 1 1 937641 0.3353438 0.47450973 937641 1
## 2 1 1136887 0.2458571 0.60931719 1136887 1
## 3 1 2116240 0.8232859 0.08444933 2116240 1
## 4 1 2310562 0.4932038 0.30697357 2310562 1
## 5 1 2681715 0.6053916 0.21796358 2681715 1
## 6 1 2917484 0.1944431 0.71120743 2917484 1is.data.frame(manhattanrObject[["data"]])## [1] TRUEThis manhattanrObject which is of class manhattanr can also be passed to the manhattanly function:
manhattanly(manhattanrObject)We can specify more annotations in the data using the snp, gene, annotation1 and annotation2 arguments:
# create an object of class `manhattanr`
manhattanrObject <- manhattanr(HapMap, snp = "SNP", gene = "GENE",
annotation1 = "DISTANCE", annotation2 = "EFFECTSIZE")
# the annotation columns are now part of the data.frame
head(manhattanrObject[["data"]])## CHR BP P SNP GENE DISTANCE EFFECTSIZE logp
## 1 1 937641 0.3353438 rs9697358 ISG15 1068 -0.0946 0.47450973
## 2 1 1136887 0.2458571 rs34945898 TNFRSF4 0 -0.0947 0.60931719
## 3 1 2116240 0.8232859 rs12034613 FP7162 0 -0.0741 0.08444933
## 4 1 2310562 0.4932038 rs4648633 MORN1 0 0.0146 0.30697357
## 5 1 2681715 0.6053916 rs4430271 MMEL1 127427 0.1234 0.21796358
## 6 1 2917484 0.1944431 rs6685625 ACTRT2 10421 0.1979 0.71120743
## pos index
## 1 937641 1
## 2 1136887 1
## 3 2116240 1
## 4 2310562 1
## 5 2681715 1
## 6 2917484 1is.data.frame(manhattanrObject[["data"]])## [1] TRUESimilarly the data used for the Q-Q plot can be created using the qqr function:
qqrObject <- qqr(HapMap)
str(qqrObject)## List of 6
## $ data :'data.frame': 14412 obs. of 3 variables:
## ..$ P : num [1:14412] 6.75e-10 3.41e-09 3.95e-09 4.71e-09 5.02e-09 ...
## ..$ OBSERVED: num [1:14412] 9.17 8.47 8.4 8.33 8.3 ...
## ..$ EXPECTED: num [1:14412] 4.46 3.98 3.76 3.61 3.51 ...
## $ pName : chr "P"
## $ snpName : logi NA
## $ geneName : logi NA
## $ annotation1Name: logi NA
## $ annotation2Name: logi NA
## - attr(*, "class")= chr "qqr"head(qqrObject[["data"]])## P OBSERVED EXPECTED
## 4346 6.75010e-10 9.170690 4.459754
## 4347 3.41101e-09 8.467117 3.982633
## 4344 3.95101e-09 8.403292 3.760784
## 4338 4.70701e-09 8.327255 3.614656
## 4342 5.02201e-09 8.299122 3.505512
## 4341 6.22801e-09 8.205651 3.418362This qqrObject which is of class qqr can also be passed to the qqly function (we omit the plot here for the sake of size of the rendered vignette):
qqly(qqrObject)Similarly the data used for the volcanor plot can be created using the volcanor function:
volcanorObject <- volcanor(HapMap,
p = "P",
effect_size = "EFFECTSIZE",
snp = "SNP",
gene = "GENE")
str(volcanorObject)## List of 8
## $ data :'data.frame': 14412 obs. of 5 variables:
## ..$ EFFECTSIZE: num [1:14412] -0.0946 -0.0947 -0.0741 0.0146 0.1234 ...
## ..$ P : num [1:14412] 0.335 0.246 0.823 0.493 0.605 ...
## ..$ SNP : chr [1:14412] "rs9697358" "rs34945898" "rs12034613" "rs4648633" ...
## ..$ GENE : chr [1:14412] "ISG15" "TNFRSF4" "FP7162" "MORN1" ...
## ..$ LOG10P : num [1:14412] 0.4745 0.6093 0.0844 0.307 0.218 ...
## $ pName : chr "P"
## $ effectName : chr "EFFECTSIZE"
## $ xlabel : chr "EFFECTSIZE"
## $ snpName : chr "SNP"
## $ geneName : chr "GENE"
## $ annotation1Name: logi NA
## $ annotation2Name: logi NA
## - attr(*, "class")= chr "volcanor"head(volcanorObject[["data"]])## EFFECTSIZE P SNP GENE LOG10P
## 1 -0.0946 0.3353438 rs9697358 ISG15 0.47450973
## 2 -0.0947 0.2458571 rs34945898 TNFRSF4 0.60931719
## 3 -0.0741 0.8232859 rs12034613 FP7162 0.08444933
## 4 0.0146 0.4932038 rs4648633 MORN1 0.30697357
## 5 0.1234 0.6053916 rs4430271 MMEL1 0.21796358
## 6 0.1979 0.1944431 rs6685625 ACTRT2 0.71120743This can be directly passed to the volcanoly function, with additional arguments:
volcanoly(volcanorObject, effect_size_line = c(-1, 0.5))These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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